Multi‐sensor medical image fusion using pyramid‐based DWT: a multi‐resolution approach

Nair, Rekha R; Singh, Tripty

doi:10.1049/iet-ipr.2018.6556

Cited by 58 publications

(22 citation statements)

References 22 publications

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“…Table 4 depicts the comparison of performance metrics with state‐of‐art methods. In this work, we have chosen five state‐of‐art methods, namely, pyramid based discrete wavelet transform (PBDWT), 27 non subsampled shearlet transform (NSST), 19 optimal wavelet coupled images factorization (OWCIF), 28 DOBWIF, 29 and the proposed method. These state‐of‐art methods are evaluated with different performance metrics such as SD ( S dv ), structural similarity index ( S SI ), feature similarity index ( F SI ), mutual information ( M if ), fusion quality ( F q ), and RMSE.…”

Section: Resultsmentioning

confidence: 99%

Modified Global Flower Pollination Algorithm‐based image fusion for medical diagnosis using computed tomography and magnetic resonance imaging

Sumithra

Malathi²

2020

Int J Imaging Syst Tech

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Recently, the computed tomography (CT) and magnetic resonance imaging (MRI) medical image fusion have turned into a challenging issue in the medical field. The optimal fused image is a significant component to detect the disease easily. In this research, we propose an iterative optimization approach for CT and MRI image fusion. Initially, the CT and MRI image fusion is subjected to a multilabel optimization problem. The main aim is to minimize the data and smoothness cost during image fusion. To optimize the fusion parameters, the Modified Global Flower Pollination Algorithm is proposed. Here, six sets of fusion images with different experimental analysis are evaluated in terms of different evaluation metrics such as accuracy, specificity, sensitivity, SD, structural similarity index, feature similarity index, mutual information, fusion quality, and root mean square error (RMSE). While comparing to state‐of‐art methods, the proposed fusion model provides best RMSE with higher fusion performance. Experiments on a set of MRI and CT images of medical data set show that the proposed method outperforms a very competitive performance in terms of fusion quality.

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Section: Resultsmentioning

confidence: 99%

Modified Global Flower Pollination Algorithm‐based image fusion for medical diagnosis using computed tomography and magnetic resonance imaging

Sumithra

Malathi²

2020

Int J Imaging Syst Tech

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“…Finally, the fused image is reconstructed through the corresponding inverse transform. The MST methods mainly contain the Laplacian pyramid (LP) [6], the wavelet transform (WT) [27,34], the non-subsampled contourlet transform (NSCT) [49], and the non-subsampled shearlet transform (NSST) [4,23,38]. However, if the MST method performs without other fusion measures, some unexpected block effect may appear [39].…”

Section: Current Challenges In Multimodal Image Fusionmentioning

confidence: 99%

Multimodal medical image fusion algorithm in the era of big data

Tan

Tiwari

Pandey

et al. 2020

Neural Comput & Applic

117

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In image-based medical decision-making, different modalities of medical images of a given organ of a patient are captured. Each of these images will represent a modality that will render the examined organ differently, leading to different observations of a given phenomenon (such as stroke). The accurate analysis of each of these modalities promotes the detection of more appropriate medical decisions. Multimodal medical imaging is a research field that consists in the development of robust algorithms that can enable the fusion of image information acquired by different sets of modalities. In this paper, a novel multimodal medical image fusion algorithm is proposed for a wide range of medical diagnostic problems. It is based on the application of a boundary measured pulse-coupled neural network fusion strategy and an energy attribute fusion strategy in a non-subsampled shearlet transform domain. Our algorithm was validated in dataset with modalities of several diseases, namely glioma, Alzheimer's, and metastatic bronchogenic carcinoma, which contain more than 100 image pairs. Qualitative and quantitative evaluation verifies that the proposed algorithm outperforms most of the current algorithms, providing important ideas for medical diagnosis.

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“…Functional images reflect information of blood flow and blood activity [2], for instance, positron emission CT (PET) and single-photon emission CT (SPECT). Medical images with single modality do not provide sufficient information in diagnosing diseases; medical image fusion (MIF) technology provides an effective method via merging medical images with different modalities into a comprehensive MIF image to aid radiologists for better diagnosis [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Morphological Component Analysis-Based Perceptual Medical Image Fusion Using Convolutional Sparsity-Motivated PCNN

Tian

Tang

Xiao

et al. 2021

Scientific Programming

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This paper proposes a perceptual medical image fusion framework based on morphological component analysis combining convolutional sparsity and pulse-coupled neural network, which is called MCA-CS-PCNN for short. Source images are first decomposed into cartoon components and texture components by morphological component analysis, and a convolutional sparse representation of cartoon layers and texture layers is produced by prelearned dictionaries. Then, convolutional sparsity is used as a stimulus to motivate the PCNN for dealing with cartoon layers and texture layers. Finally, the medical fused image is computed via combining fused cartoon layers and texture layers. Experimental results verify that the MCA-CS-PCNN model is superior to the state-of-the-art fusion strategy.

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Multi‐sensor medical image fusion using pyramid‐based DWT: a multi‐resolution approach

Cited by 58 publications

References 22 publications

Modified Global Flower Pollination Algorithm‐based image fusion for medical diagnosis using computed tomography and magnetic resonance imaging

Modified Global Flower Pollination Algorithm‐based image fusion for medical diagnosis using computed tomography and magnetic resonance imaging

Multimodal medical image fusion algorithm in the era of big data

Morphological Component Analysis-Based Perceptual Medical Image Fusion Using Convolutional Sparsity-Motivated PCNN

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